Using waste heat from a data center cooling system to facilitate low-temperature desalination

ABSTRACT

The disclosed embodiments relate to a system that performs low-temperature desalination. During operation, the system feeds cold saline water through a liquid-cooling system in a computer data center, wherein the cold saline water is used as a coolant, thereby causing the cold saline water to become heated saline water. Next, the system feeds the heated saline water into a vacuum evaporator comprising a water column having a headspace, which is under a negative pressure due to gravity pulling on the heated saline water in the water column. This negative pressure facilitates evaporation of the heated saline water to form water vapor. Finally, the system directs the water vapor through a condenser, which condenses the water vapor to produce desalinated water.

RELATED APPLICATION

This application is a divisional application of, and hereby claimspriority under 35 U.S.C. § 120 to, pending U.S. patent application Ser.No. 15/884,851, entitled “Using Waste Heat from a Data Center CoolingSystem to Facilitate Low-Temperature Desalination,” by inventors KennyC. Gross and Sanjeev Sondur, filed on 31 Jan. 2018, which is herebyincorporated by reference.

BACKGROUND Field

The disclosed embodiments generally relate to techniques fordesalinating seawater. More specifically, the disclosed embodimentsrelate to a new technique that uses waste heat from a computer datacenter cooling system to drive a low-temperature desalination processfor seawater.

Related Art

The United Nations recently reported that 40% of the world's populationlives in regions affected by scarcity of safe water supplies. Note that97% of the water on Earth is salt water, and much of the remaining 3% isnot easily accessible because it is in the atmosphere or frozen in thepolar ice caps. With predictions that more than 3.5 billion people willlive in areas facing severe water shortages by the year 2025, a majorchallenge is to find an environmentally benign way to remove salt fromseawater.

At the same time, computer data centers are consuming more than 500terawatts of electricity worldwide. In fact, the U.S. Department ofEnergy reports that in 2016 data centers consumed 2% of the electricitygenerated in the United States, and at present, approximately 40% ofthis energy is used for cooling purposes.

However, air cooling of data centers has nearly reached the limitsallowed by the laws of physics. As heat densities continue to climb indata centers, the power consumed by cooling fans in modern servers hasbecome so great that in many cases the fan motors are consuming moreenergy than the CPU chips in the servers. Fluid is substantially moreefficient for heat removal than air, so thermal experts are predictingthat the data centers of the future will be fluid-cooled. Note thatmodern data centers typically use hybrid fluid-air cooling systems,which still use fans inside servers, but then extract the heat from theexhaust air using a large, efficient water-cooled heat-exchanger system.

Hence, it would be advantageous to develop a technique, which uses thelarge amount of waste heat generated by fluid cooling systems incomputer data centers to drive the process of desalinating seawater.

SUMMARY

The disclosed embodiments relate to a system that performslow-temperature desalination. During operation, the system feeds coldsaline water through a liquid-cooling system in a computer data center,wherein the cold saline water is used as a coolant, thereby causing thecold saline water to become heated saline water. Next, the system feedsthe heated saline water into a vacuum evaporator comprising a watercolumn having a headspace, which is under a negative pressure due togravity pulling on the heated saline water in the water column. Thisnegative pressure facilitates evaporation of the heated saline water toform water vapor. Finally, the system directs the water vapor through acondenser, which condenses the water vapor to produce desalinated water.

In some embodiments, the system feeds the cold saline water through thecondenser prior to feeding the cold saline water into the liquidcooling-system, wherein the condenser uses the cold saline water tocondense the water vapor.

In some embodiments, after the cold saline water feeds through thecondenser, the system feeds the cold saline water through an inlet heatexchanger, which uses unevaporated heated saline water obtained from thevacuum evaporator to preheat the cold saline water prior to feeding thecold saline water into the vacuum evaporator.

In some embodiments, a control unit in the system receives telemetrydata from the data center through a telemetry harness. This control unituses the received telemetry data to optimize the desalination efficiencyof the vacuum evaporator and the computational performance of thecomputer data center by scheduling jobs having different priorities inthe computer data center to control variations in an aggregate thermalload of the computer data center. This indirectly controls variations ina temperature of the heated saline water, which affects the desalinationefficiency of the vacuum evaporator.

In some embodiments, while scheduling the different priority jobs, thecontrol unit makes a tradeoff between the desalination efficiency andcomputational performance for the different priority jobs.

In some embodiments, the control unit enables a user to adjust thetradeoff between the desalination efficiency and the computationalperformance.

In some embodiments, while optimizing the desalination efficiency andthe computational performance, the control unit additionally controls aflow rate through the liquid-cooling system in the computer data center.

In some embodiments, while optimizing the desalination efficiency andthe computational performance, the control unit uses a multiple-input,multiple-output (MIMO) control strategy based on a multivariate stateestimation technique (MSET) to optimize the tradeoff between thedesalination efficiency and the computational performance.

In some embodiments, the desalination efficiency is optimized byminimizing peaks and valleys in an aggregate computational load for thedifferent priority jobs in the computer data center.

In some embodiments, the telemetry data includes one or more of thefollowing measured values for processors in the computer data center:power consumption parameters; temperatures; and processor performanceparameters.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an exemplary low-temperature desalination system inaccordance with the disclosed embodiments.

FIG. 2 illustrates a control system that balances desalinationefficiency with computational throughput in accordance with thedisclosed embodiments.

FIG. 3 maps the increase in water temperature at two exemplary fixedflow rates in relation to individual rack payload in kilowatts inaccordance with the disclosed embodiments.

FIG. 4 presents a flow chart illustrating the process of operating thelow-temperature desalination system in accordance with the disclosedembodiments.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present embodiments, and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present embodiments. Thus, the presentembodiments are not limited to the embodiments shown, but are to beaccorded the widest scope consistent with the principles and featuresdisclosed herein.

The data structures and code described in this detailed description aretypically stored on a computer-readable storage medium, which may be anydevice or medium that can store code and/or data for use by a computersystem. The computer-readable storage medium includes, but is notlimited to, volatile memory, non-volatile memory, magnetic and opticalstorage devices such as disk drives, magnetic tape, CDs (compact discs),DVDs (digital versatile discs or digital video discs), or other mediacapable of storing computer-readable media now known or later developed.

The methods and processes described in the detailed description sectioncan be embodied as code and/or data, which can be stored in acomputer-readable storage medium as described above. When a computersystem reads and executes the code and/or data stored on thecomputer-readable storage medium, the computer system performs themethods and processes embodied as data structures and code and storedwithin the computer-readable storage medium. Furthermore, the methodsand processes described below can be included in hardware modules. Forexample, the hardware modules can include, but are not limited to,application-specific integrated circuit (ASIC) chips, field-programmablegate arrays (FPGAs), and other programmable-logic devices now known orlater developed. When the hardware modules are activated, the hardwaremodules perform the methods and processes included within the hardwaremodules.

Overview

The disclosed embodiments provide a telemetry-controlled system thatallows the waste heat from the computer servers to be recycledefficiently to help solve a growing global shortage of drinkable water.This system integrates intelligent power monitoring (IPM) telemetry withboth power-aware job scheduling and intelligent control of cooling fluidto facilitate a new low-temperature, low-pressure desalination process,which converts salt water into drinking water with zero added carboncost. For fluid-cooled data centers, which are located near an ocean,and for modular data centers, which can always be deployed near anocean, this new integrated system exploits low-cost cooling using coldseawater and also leverages the “free” waste heat generated by computerservers by using an optimal control scheme that produces drinking andirrigation water as a byproduct.

This system integrates three elements into a novel system that providesefficient cooling for data centers while turning seawater into drinkingwater. The three elements include: (1) an IPM telemetry system; (2) acontinuous system telemetry harness (CSTH); and (3) a low-temperaturedesalination system.

The IPM telemetry system performs real-time monitoring for all of thetemperatures, voltages, currents, power metrics, and fan speeds insidethe computer servers in a data center. In doing so, the IPM telemetrysystem facilitates real-time assessment of the dynamic power levels ofboth servers and storage technology throughout the data center, even inthe absence of a network of distributed hardware power meters.

The CSTH provides a software application that enables power-aware jobscheduling for data center jobs to produce a relatively flat aggregatethermal load for the data center when it is desirable to optimize theefficiency of the desalination process. A novel machine-learning controltechnique (described below) allows an end customer to “turn a knob” toeither achieve maximum computational throughput with some reduction indesalination efficiency, or achieve maximum desalination efficiency withsome reduction in completion times for low- and medium-priorityworkloads. (Note that high-priority jobs always complete as soon aspossible under either “efficiency preference” setting.)

This low-temperature desalination system operates differently from aconventional thermal desalination system, which uses a coal-fired plant(or nuclear plant) to boil seawater, and then condenses the resultingvapor. This conventional approach has disadvantages because coal cancause a significant amount of pollution, and nuclear plants cost manybillions of dollars and involve safety-critical systems, which rely onhigh-pressure steam containment.

Fortunately, a new low-temperature, low-pressure desalination processhas recently been developed. This new approach is quite simple. If apipe is filled with water, and then sealed on one end, and then the openend is left underwater and the sealed end is put up in the air, thewater column “hangs” under its own weight and creates a vacuum at thetop (the sealed end). The higher the sealed end, the greater the vacuum.

Note that seawater vaporizes quite easily and at relatively lowtemperatures when under vacuum. Hence, the heat source only needs to be40-50° C. to provide good desalination efficiency. Also note thatbecause the vacuum is gravity driven, this new technique requires nocomplex mechanical vacuum generators. The entire system operatespassively; it just needs a good source of free waste heat. For detailsabout how such a low-temperature desalination system operates, pleasesee U.S. Pat. No. 8,080,138, entitled “Desalination Using Low-GradeThermal Energy,” by inventors Nagamany Nirmalakhandan, et al., filed on12 Dec. 2007, issued 20 Dec. 2011, which is hereby incorporated byreference.

Computer data centers provide such a source of waste heat. Data centerspresently produce gigawatts of waste heat, which is presently beingdumped into the atmosphere. (Note that one data center uses the sameamount of electricity as a city of 100,000 people.) Moreover, therepresently exist tens of thousands of data centers around the world, manyof which are located in coastal cities with unlimited volumes of saltwater within easy pumping distance. The fluid-air heat exchangers thatare already being built into such data centers can be used tosufficiently concentrate the waste heat from the data centers tofacilitate efficient large-scale conversion of seawater into potabledrinking water, through a low-temperature desalination process, whichonly requires the seawater to be heated to a temperature of about 40-50°C.

This energy-aware scheduling technique operates by optimally sequencinghigh-, medium-, and low-priority jobs to run on computational assets, ina manner that does not violate service level agreements for workloadcompletion, and also optimizes the efficiency of turning salt water intoclean drinking water by intelligently capturing the “free” waste heatfrom computer server systems, which is presently ejected into theatmosphere. Note that this new low-temperature desalination techniqueavoids the complexity and danger of conventional high-pressuredesalination systems, wherein seawater must be boiled and condensed, andalso the enormous energy costs associated with the heat sources forconventional boiling-based desalination.

Having a uniform flow of warm water is a key to maintaining gooddesalination efficiency. Hence, the integrated system uses intelligentworkload scheduling coupled with telemetry-driven flow rate control tocontrol the exit temperature of the cooling water from the data center.Moreover, the integrated system allows the end user to “turn a knob” tooptimize overall data center operations to facilitate maximum computingthroughput performance, or to facilitate maximum drinking-waterpurification.

As a further efficiency enhancement, the system can direct the coldseawater through the condenser first, which warms the seawater slightlybefore it is used to cool the hot thermal exhaust air from the racks ofcomputing equipment. Note that warming the seawater slightly in thecondenser makes the seawater somewhat less efficient for server cooling.However, this efficiency loss is more than offset by the efficiency gainduring the desalination process.

For maximum desalination efficiency, it is desirable to get as much heatinto the water as we can, so we warm it up first in the condenser, andthen preheat it in an inlet heat exchanger before it is heated up in thehot-air exhaust of the computer data center.

For maximum computational performance, we turn up the flow rate for thewater, which causes the computers to stay cooler, which enables thecomputers to maximize computational performance. However, the water doesnot get as warm at higher flow rates, so desalination efficiency drops.The new integrated system allows a user to adjust this tradeoff by“turning a knob” to optimize between more clean water output or highercompute performance. (Note that a by-product of this process will betruckloads of sea salt, which has a much higher profit margin thanregular table salt.)

As mentioned above, causing the computers to stay cooler facilitiesmaximizing computational performance. Note that all enterprise computingcentral-processing units (CPUs), including those from Intel, AMD,Oracle, and IBM, presently use real-time dynamic-voltage-and-frequencyscaling (DVFS) to adjust the frequency and voltage of the CPUs to avoidwasteful “leakage power” in the CPUs. Leakage power is exponentiallydriven by CPU temperatures, so that DVFS “slows down” the operatingfrequency of CPUs when the CPU temperatures are warmer, and “speeds up”the operating frequency when the CPU temperatures are cooler.Consequently, there is an increase in computational performance andthroughput when the CPUs are cooler, and a performance penalty when theCPUs are warmer, for all modern enterprise computing systems. (See“Leakage-Aware Workload and Cooling Management for Improving ServerEnergy Efficiency,” M. Zapater, O. Tuncer, J. L. Ayala, J. M. Moya, K.Vaidyanathan, K. C. Gross, and A. K. Coskun, Transactions on Paralleland Distributed Systems Journal, Jun. 29, 2014.) Because of thisrelationship between CPU temperatures (which are measured by the CSTHfor every CPU and every core in all of the computer systems being cooledby salt water and used as separate inputs to the MIMO controller) andCPU performance, the MIMO controller allows the end user to select abalance point between maximum desalination efficiency and maximum CPUperformance.

FIG. 1 illustrates an exemplary low-temperature desalination system 100in accordance with the disclosed embodiments. During operation,low-temperature desalination system 100 receives cold saline water 103from inlet 102. This cold saline water 103 first feeds through acondenser 104, wherein it is used to cool water vapor 119 produced byvacuum evaporator 116. The cooled water vapor condenses to form freshwater, which is collected in fresh water reservoir 106. The condenser104 also causes the cold saline water 103 to be converted into slightlywarmed saline water 108, which feeds into an inlet heat exchanger 110.Inlet heat exchanger 110 uses unevaporated heated saline water 120received from vacuum evaporator 116 to preheat the slightly warmedsaline water 108 to form preheated saline water 111, which then feedsinto a data center cooling system 112. The unevaporated heated salinewater 120 then exits the system through outlet 124. Data center coolingsystem 112 further heats the preheated saline water 111 to produceheated saline water 114, which feeds into vacuum evaporator 116. Asmentioned above, vacuum evaporator 116 uses the heated saline water toproduce water vapor 119, which feeds into condenser 104, which condensesthe water vapor 119 to form fresh water, which is stored in fresh waterreservoir 106.

As mentioned above, the operation of low-temperature desalination system100 is controlled by a pump 122, and also by a number of flow valves107, 117 and 118.

This type of low-temperature desalination system can be implemented in astandard “modular data center” (MDC) unit. Existing MDC units alreadyhave a high-efficiency fluid-cooling system with two large fire-hoseconnections, one for cold water going in, and one for hot water goingout. Essentially all of the waste heat generated inside the MDC isremoved via the cooling water, minus a very small amount of heat thatescapes from the six insulated sides of the MDC. Although thelow-temperature desalination system is described in the context of anMDC unit, this type of system has no scalability limitations, and cantherefore be used in any larger or smaller data center with aliquid-cooling system.

The disclosed embodiments combine real-time telemetry of thecooling-water inlet temperature with machine-learning-based control ofthe cooling system flow rate (based on MSET) to control the exittemperature of the cooling water from the data center. Note that, in anyconventional system with a fluid-cooled heat exchanger, if the flow rateof the cooling fluid is increased, then the system gets cooler, and theexit temperature of the cooling fluid is cooler. Conversely, if the flowrate of the cooling fluid is slowed down, the system does not get cooledas much, and the exit temperature of the slower-moving cooling fluid ishigher. For example, FIG. 3 maps the increase in water temperature attwo exemplary fixed flow rates (45 GPM and 65 GPM) in relation toindividual rack payloads in kilowatts in accordance with the disclosedembodiments. Note that, in order to optimize the efficiency of thedesalination process, it is preferable to have a uniform heat fluxinside the data center, so that the cooling-water exit temperature canbe kept near the “sweet spot” for desalination efficiency.

However, many data center workloads are quite dynamic over time. Toremedy this problem, we can smooth out these dynamics using a controlsystem 200, which implements a power-aware intelligent job scheduler asis illustrated in FIG. 2. This job scheduler exploits user-definedpriority flags and divides the compute jobs into three prioritycategories: low, medium, and high. The system, however, can generallyuse any continuum of priorities.

The low-, medium- and high-priority jobs are stored in corresponding jobqueues 202. Low-priority jobs typically include long-runningsimulations, batch numerical analysis workloads, background databasebuilds, operating housecleaning utilities, and other types of long-livedjobs. Medium-priority jobs typically include jobs for which a humaneventually wants to access and manually interact with the results, butdoes not expect those results to be produced in seconds or minutes.High-priority jobs are typically business-critical applications, orinvolve direct human-interactive processes, such as searching,spreadsheet manipulation, database mining, etc. These high-priority jobsare always dispatched as soon as possible when the computing assetsbecome available. Note that if all jobs were treated as high-priority,there would be significant dynamics in the overall workload withcorresponding peaks and troughs in energy flux, which would causefluctuations in the cooling water exit temperature if the flow rate isconstant.

The dispatching of jobs 205 is controlled by an intelligent jobscheduler 204, which includes a power-aware load-balancing module 206.Note that jobs 205 are dispatched to various processors 221-223 in thedata center. Moreover, processors 221-223 include correspondingintelligent power monitors (IPMs) 231-233, which communicate variousparameter values, such as power consumption values, temperatures andperformance characteristics 210 for processors 221-223 to a multivariatesupervisory control module 208.

Multivariate supervisory control module 208 then uses these parametervalues along with customer preferences 209 to control both intelligentjob scheduler 204 and flow-rate controller for liquid cooling 212, whichcontrols pump 122 and flow valves 107, 117 and 118 withinlow-temperature desalination system 100. In doing so, multivariatesupervisory control module 208 uses a novel multiple-input,multiple-output (MIMO) control scheme that uses the multivariate stateestimation technique (MSET) to simultaneously and optimally controlcooling-fluid flow rate and energy-aware load scheduling. (Foradditional details about MIMO control schemes, please see U.S. Pat. No.5,920,478, entitled “Multiple-Input, Multiple-Output Generic InteractingController,” by inventor Mark K. Ekblad, et al., filed on Jun. 27, 1997,which is hereby incorporated by reference. For additional details aboutMSET, please see Gross, K. C., R. M. Singer, S. W. Wegerich, J. P.Herzog, R. Van Alstine, and F. K. Bockhorst, “Application of aModel-based Fault Detection System to Nuclear Plant Signals,” Proc. 9thIntl. Conf. on Intelligent Systems Applications to Power Systems, Seoul,Korea, 1997.)

One aspect of the disclosed embodiments is that the real-time power flux(monitored via the IPM) from the computing system components is inputinto the intelligent job scheduler 204, which inserts small delays inthe dispatching of the medium-priority jobs, and larger delays in thedispatching of the low-priority jobs. In this way, the intelligent jobscheduler 204 uses the low-priority and medium-priority jobs to “fillin” troughs in overall thermal flux for a much more balanced and stableaggregate workload and, hence, a more stable thermal flux. (Foradditional details about intelligent job scheduling, please see U.S.Pat. No. 8,555,283, entitled “Temperature-Aware and Energy-AwareScheduling in a Computer System,” by inventors Ayse K. Coskun, Kenny C.Gross and Keith A. Whisnant, filed on Nov. 12, 2007, which is herebyincorporated by reference.)

To the extent there are still overall variations in aggregate thermalflux for the data center (e.g., if there are sustained periods with lowutilization such as in the middle of the night or on the weekend), thesystem can control the cooling water flow to maintain the desireduniform output coolant temperature for the integrated low-pressuredesalination system.

Desalination Process

FIG. 4 presents a flow chart illustrating the process of operating thelow-temperature desalination system in accordance with the disclosedembodiments. During operation, the system first obtains cold salinewater (step 402). Next, the system feeds the cold saline water through acondenser to produce slightly warmed saline water (step 404). The systemthen feeds the slightly warmed saline water through an inlet heatexchanger, which uses unevaporated heated saline water obtained from avacuum evaporator to preheat the slightly warmed saline water (step406). Next, the system feeds the preheated saline water through aliquid-cooling system in a computer data center, wherein the preheatedsaline water is used as a coolant, thereby causing the preheated salinewater to become heated saline water (step 408). The system then feedsthe heated saline water into a vacuum evaporator comprising a watercolumn having a headspace, which is under a negative pressure due togravity pulling on the heated saline water in the water column, whereinthe negative pressure facilitates evaporation of the heated saline waterto form water vapor (step 410). Finally, the system directs the watervapor through the condenser, which condenses the water vapor to producedesalinated water, wherein the condenser uses the cold saline water tocondense the water vapor (step 412).

Various modifications to the disclosed embodiments will be readilyapparent to those skilled in the art, and the general principles definedherein may be applied to other embodiments and applications withoutdeparting from the spirit and scope of the present invention. Thus, thepresent invention is not limited to the embodiments shown, but is to beaccorded the widest scope consistent with the principles and featuresdisclosed herein.

The foregoing descriptions of embodiments have been presented forpurposes of illustration and description only. They are not intended tobe exhaustive or to limit the present description to the formsdisclosed. Accordingly, many modifications and variations will beapparent to practitioners skilled in the art. Additionally, the abovedisclosure is not intended to limit the present description. The scopeof the present description is defined by the appended claims.

What is claimed is:
 1. A method for performing low-temperaturedesalination, comprising: obtaining cold saline water, wherein the coldsaline water is ocean water; feeding the cold saline water through aliquid-cooling system in a computer data center, wherein the cold salinewater is used as a coolant, thereby causing the cold saline water tobecome heated saline water; feeding the heated saline water into avacuum evaporator comprising a water column having a headspace, which isunder a negative pressure due to gravity pulling on the heated salinewater in the water column, wherein the negative pressure facilitatesevaporation of the heated saline water to form water vapor; directingthe water vapor through a condenser, which condenses the water vapor toproduce desalinated water; receiving telemetry data from the data centerthrough a telemetry harness; and using the received telemetry data tooptimize a desalination efficiency of the vacuum evaporator and acomputational performance of the computer data center by scheduling jobshaving different priorities in the computer data center to controlvariations in an aggregate thermal load of the computer data center,thereby indirectly controlling variations in a temperature of the heatedsaline water, which affects the desalination efficiency of the vacuumevaporator, wherein scheduling the different priority jobs involvesmaking a tradeoff between the desalination efficiency and computationalperformance for the different priority jobs.
 2. The method of claim 1,wherein the method further comprises feeding the cold saline waterthrough the condenser prior to feeding the cold saline water into theliquid cooling-system, wherein the condenser uses the cold saline waterto condense the water vapor.
 3. The method of claim 2, wherein after thecold saline water feeds through the condenser and becomes warmed salinewater, the method further comprises feeding the warmed saline waterthrough an inlet heat exchanger, which uses unevaporated heated salinewater obtained from the vacuum evaporator to preheat the swarmed salinewater prior to feeding the preheated saline water into the liquidcooling system.
 4. The method of claim 1, wherein the method furthercomprises enabling a user to adjust the tradeoff between thedesalination efficiency and the computational performance.
 5. The methodof claim 1, wherein optimizing the desalination efficiency and thecomputational performance additionally involves controlling a flow ratethrough the liquid-cooling system in the computer data center.
 6. Themethod of claim 1, wherein optimizing the desalination efficiency andthe computational performance additionally involves using amultiple-input, multiple-output (MIMO) control strategy based on amultivariate state estimation technique (MSET) to optimize the tradeoffbetween the desalination efficiency and the computational performance.7. The method of claim 1, wherein the desalination efficiency isoptimized by minimizing peaks and valleys in an aggregate computationalload for the different priority jobs in the computer data center.
 8. Themethod of claim 1, wherein the telemetry data includes one or more ofthe following measured values for processors in the computer datacenter: power consumption parameters; temperatures; and processorperformance parameters.